Inspired by few-shot learning paradigm [1]}, [2]}, which aims to learn-to-learn a model for a novel task with only a limited number of samples, few-shot segmentation has received considerable attention. Following the success of [3]}, prototypical networks [2]} and numerous other works [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]} proposed to utilize
a prototype extracted from support samples, which is used to refine the query features to contain the relevant support information.
In addition, inspired by [17]} that observed the use of high-level features leads to a performance drop, [18]} proposed to utilize high-level features by computing a prior map which takes maximum score within a correlation map. Many variants [14]}, [20]} extended this idea of utilizing prior maps to guide the feature learning.
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